Salient Object Detection through Over-Segmentation

In this paper we present a salient object detection model from an over-segmented image. The input image is initially segmented by the mean-shift segmentation algorithm and then over-segmented by a quad mesh to even smaller segments. Such segmented regions overcome the disadvantage of using patches or single pixels to compute saliency. Segments that are similar and spread over the image receive low saliency and a segment which is distinct in the whole image or in a local region receives high saliency. We express this as a color compactness measure which is used to derive saliency level directly. Our method is shown to outperform six existing methods in the literature using a saliency detection database containing images with human-labeled object contour ground truth. The proposed saliency model has been shown to be useful for an image retargeting application.